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dc.contributor.author | Zinetullina, Altyngul | |
dc.contributor.author | Yang, Ming | |
dc.contributor.author | Khakzad, Nima | |
dc.contributor.author | Golman, Boris | |
dc.contributor.author | Li, Xinhong | |
dc.date.accessioned | 2021-07-14T08:26:41Z | |
dc.date.available | 2021-07-14T08:26:41Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Zinetullina, A., Yang, M., Khakzad, N., Golman, B., & Li, X. (2021). Quantitative resilience assessment of chemical process systems using functional resonance analysis method and Dynamic Bayesian network. Reliability Engineering and System Safety, 205, [107232]. https://doi.org/10.1016/j.ress.2020.107232 | en_US |
dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/5571 | |
dc.description.abstract | The emergent hazards of chemical process systems cannot be wholly identified and are highly uncertain due to the complicated technical-human-organizational interactions. Under uncertain and unpredictable circumstances, resilience becomes an essential property of a chemical process system that helps it better adapt to disruptions and restore from surprising damages. The resilience assessment needs to be enhanced to identify the accident's root causes on the level of technical-human-organizational interactions, and development of the specific resilience attributes to withstand or recover from the disruptions. The outcomes of resilience assessment are valuable to identify potential design or operational improvements to ensure complex process system functionality and safety... | en_US |
dc.language.iso | en | en_US |
dc.publisher | Reliability Engineering and System Safety | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
dc.subject | resilience assessment | en_US |
dc.subject | chemical process systems | en_US |
dc.subject | dynamic Bayesian network | en_US |
dc.subject | Open access | en_US |
dc.title | QUANTITATIVE RESILIENCE ASSESSMENT OF CHEMICAL PROCESS SYSTEMS USING FUNCTIONAL RESONANCE ANALYSIS METHOD AND DYNAMIC BAYESIAN NETWORK | en_US |
dc.type | Article | en_US |
workflow.import.source | science |
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